In addition to applying PatchNCE loss on images from domain \(\mathcal {X}\)  we also apply it on images from domain \(\mathcal {Y}\)  (referred to as identity loss) when using Contrastive Unpaired Translation (CUT) model where \(\lambda _X=\lambda _Y=1\) . This is done in order to prevent the generator from making unnecessary changes - we calculate PatchNCE loss between a translated image and a real image taken from the target domain dataset.
A lighter and faster method called FastCUT is also provided in which case \(\lambda _X=10\)  in order to compensate for absence of the regularizer, i.e. for \(\lambda _Y=0\) . In the experimental phase we show that although the model is faster and lighter to train, the absence of the regularizer can result in significant oscillations in model's performance during the training and potentially cause it to achieve subpar final results. FastCUT is designed for usage when time to train is restricted or GPU memory limitations are present as it can achieve satisfactory results similar to CycleGAN [1]}, while requiring significantly less memory and time to train.
